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An Experimental Ethics Approach to Robot Ethics Education The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-20) An Experimental Ethics Approach to Robot Ethics Education Tom Williams Qin Zhu Daniel Grollman Colorado School of Mines Colorado School of Mines Plus One Robotics Golden, CO 80402 Golden, CO 80402 Boulder, CO 80301 Abstract those concepts to analyze applications of AI and Robotics. But moreover, such courses may also cover computational We propose an experimental ethics-based curricular module approaches to moral decision making; psychological theo- for an undergraduate course on Robot Ethics. The proposed module aims to teach students how human subjects research ries of moral decision making and blame; and methods for methods can be used to investigate potential ethical concerns experimentally investigating ethical issues. This requires in- arising in human-robot interaction, by engaging those stu- structors with broad interdisciplinary backgrounds, and ped- dents in real experimental ethics research. In this paper we agogical methods that draw on these disparate disciplines. describe the proposed curricular module, describe our imple- In this work, we explore pedagogical techniques aimed at mentation of that module within a Robot Ethics course of- improving students achievement of learning objectives that fered at a medium-sized engineering university, and statis- sit at this confluence of disciplines. Specifically, we present tically evaluate the effectiveness of the proposed curricular and analyze the efficacy of a Robot Ethics course module in module in achieving desired learning objectives. While our results do not provide clear evidence of a quantifiable ben- which students participate as experimenters in experimen- efit to undergraduate achievement of the described learning tal robot ethics research, which requires them to simultane- objectives, we note that the module did provide additional ously learn methodological approaches to the study of exper- learning opportunities for graduate students in the course, as imental robot ethics, and then use those methods to engage they helped to supervise, analyze, and write up the results of with key theoretical concepts from robot ethics (in our case, this undergraduate-performed research experiment. robots’ normative influence). Moreover, in this work we not only analyze the efficacy of this module, but additionally interrogate how this efficacy depends on the role that each Introduction student played in the research process. Computer Science educators are increasingly acknowledg- This educational research effort thus involves two nested ing that it is insufficient for the Computer Science curricu- levels of experimentation: a randomized controlled experi- lum to entirely focus on technical issues relating to comput- ment in which the research participants were undergraduate ing and programming. Rather, for Computer Science stu- students enrolled in a Robot Ethics class and the researchers dents to be effective practitioners after graduation, their ed- were the course staff, and a second randomized controlled ucation must include key concepts from the arts, social sci- experiment in which the research participants were under- ences, and humanities, so that students not only have the graduate students sampled from across the university, and technical knowledge necessary to implement and analyze the researchers were both the Robot Ethics students and computational systems, but also have the knowledge from their instructors. In this paper, we will focus on the first of those other fields necessary to decide, on ethical grounds, these experiments (the AI Education research effort), leav- whether they should implement those systems, and if so, ing many of the details of the second experiment (the ex- how they should go about designing and evaluating the ef- perimental Robot Ethics research effort) to publication else- fectiveness of those systems. This is especially true in the where. fields of Artificial Intelligence and Human-Robot Interac- As described in this paper, our analysis yielded mixed re- tion, in which myriad ethical concerns have captured the sults with respect to the efficacy of this experimental course public’s attention, and in which human interactivity necessi- module. While we did not find any evidence that participat- tates design and evaluation techniques not otherwise taught ing in experimental ethics research was any more effective in the Computer Science curriculum. than merely listening to a traditional lecture about the re- Appropriate teaching of these skills is made especially search effort and its goals in general, we did find that for par- challenging due to the interdisciplinary nature of the sub- ticipants that participated in the research effort, the role they ject matter. AI Ethics education must clearly cover key con- played in the research effort may indeed have affected their cepts from ethics and moral philosophy as well as the use of learning of the concepts explored in that research. More- Copyright c 2020, Association for the Advancement of Artificial over, as we will describe, the module provided additional Intelligence (www.aaai.org). All rights reserved. learning opportunities to the small number of graduate stu- 13485 dents in the course, who supervised undergraduate students, The same argument can be made for other similar media- and directly contributed to the data analysis and writing of based pedagogies such as using movies to teach AI and robot the scientific paper submission resulting from the proposed ethics. However, from the perspective of moral psychology, curricular module. there is a gap between ethical reasoning (e.g., knowing what is good vs. bad and why) and ethical action (e.g., some- Background one is committed to do good) (Rest et al. 1999). Effective professional ethics education requires future professionals to AI Ethics Education relate their moral learning experience to their own everyday Explicit discussions of AI or “expert systems” in computer personal and professional experience (or how they actually ethics education literature and textbooks can be traced back do things) (Martin 2000). For computer science students, it to the 1990s, although most of these discussions are often is crucial to reflect on how their moral learning experience speculative reflections about broader “macro” and social im- is relevant to their everyday, practical experience, empathiz- pacts of AI on humans, cultures, and societies. For instance, ing with potential users and their needs, and reflecting on Forester and Morrison (1994) hold a humanistic and specu- the (powerful) role of their expertise in shaping the soci- lative view toward the employment of AI in the society and ety. As such, it is critical to consider how real-world exam- their major concerns include: (1) whether AI is a proper goal ples and hands-on experiences with realistic AI and robotics as most AI projects are funded by the military; (2) it is a technologies may help to fill this gap. technocratic idea to employ AI in public administration, le- Carnegie Mellon’s “Artificial Intelligence Methods for gal practice, and social governance; (3) introducing AI to Social Good” course, for example, goes beyond the tradi- developing countries is another techno-fix that attempts to tional instructional approach that teaches theories of AI and remedy the symptoms without addressing the causes; and robot ethics through classroom lectures alone. Students in (4) AI degrades the human condition. the course instead acquire practical experience through re- Two curriculum design approaches have been developed search projects that employ AI methods to address press- to teach ethics in AI and robotics: (1) standalone courses ing social issues in fields such as healthcare, social welfare, (these courses can be offered in either computer science security and privacy, and environmental sustainability (Hsu or philosophy); and (2) ethics modules in AI and robotics 2018). Such hands-on experience may help students develop courses (Burton et al. 2017). Many of these curriculum de- sensitivity to the normative influence of technology on hu- velopment approaches are not so much different from tradi- mans and the society. Arguably, this model of teaching com- tional “applied ethics” approaches to teaching ethics of tech- puter science students to perform or experimentally inves- nology and engineering. For instance, in order to understand tigate ethics in a technical class has some advantages. To and discuss AI ethics issues, Burton et al. (2017) suggest that some extent, it helps make visible the values that are em- it is necessary for students to be familiar with three major bedded in the design of AI and robotic technologies. Fur- ethical theories (deontology, consequentialism or utilitarian- thermore, it creates a mindset among students that techno- ism, and virtue ethics) as tools for ethical decision-making. logical development involves value choices. Students are then invited to practice on how to use the three As science and engineering education curricula are al- ethical theories to analyze specific AI ethical situations and ready packed, integrating ethics modules into technical formulate possible courses of actions. Burton et al. (2017) courses is often more realistic for faculty. Furey and Mar- point out that teaching students the three ethical theories and tin (2018) have shared their experience with integrating a their applications in specific cases studies can be achieved in module about the ethics of algorithm development
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